We use both eGeMAPS and MFCC features to build the baseline features used in our previous work [1]}.
In the MFCC set, 24 MFCCs are extracted each 10 ms on a 30 ms window and summarized with mean and standard deviation over the emotional segment (250
 ms for AlloSat and 100 ms for SEWA) using librosa toolkit https://librosa.github.io/librosa/.
In the eGeMAPS set, we extract 23 LLDs [2]}. Mean and standard deviation of these 23 LLDs are computed over the emotional segment. This feature set is extracted with the toolkit OpenSmile [3]}. An additional binary flag denoting speaker presence extracted from speech turns, is added to acoustic feature sets extracted on SEWA only.
